NLP_depression.ipynb

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   "source": [
    "## Psychology Doc "
   ]
  },
  {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "! pip install transformers requests beautifulsoup4 pandas numpy\n",
    "! pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio===0.8.1 -f https://download.pytorch.org/whl/torch_stable.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "import torch\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#instantiate model\n",
    "tokenizer = AutoTokenizer.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')\n",
    "model = AutoModelForSequenceClassification.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_score(review):\n",
    "    tokens = tokenizer.encode(review, return_tensors='pt')\n",
    "    result = model(tokens)\n",
    "    return int(torch.argmax(result.logits))+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_statement=\"I was sick and tired of failing my exams, I'm thinking of committing suicide\"\n",
    "my_statement2=\"I am always a happy person. Received my letter of acceptance to harvard Business School and now I'm over the moon ,literally \"\n",
    "my_statement3='I am just lonely, in need of a friend '\n",
    "my_statement4='Lost my friend to a disease ,feeling sad'\n",
    "my_statement5='After relapaing back to alcoholism, I questionned my sanity'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_score(my_statement)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sentiment score 1 is very negative "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_score(my_statement2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sentiment score 5 is very possitive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_score(my_statement3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_score(my_statement4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_score(my_statement5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "text1=\"Mental health issues in school are a growing concern. Children and adolescents may experience a range of mental health challenges, including anxiety, depression, and behavioral disorders. These issues can impact academic performance, social relationships, and overall well-being. It's important for schools to prioritize mental health resources and support to promote the success and happiness of all students.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def causes(text): \n",
    "    # determines the major cause of stress an individual is facing\n",
    "    family=0\n",
    "    finance=0\n",
    "    school=0\n",
    "    relationship=0\n",
    "    work=0\n",
    "    text=text.lower()\n",
    "    text=text.split()\n",
    " \n",
    "    for i in (text):\n",
    "        if i == 'school':\n",
    "            school+=1\n",
    "        elif i == 'family':\n",
    "            family+=1\n",
    "        elif i == 'finance':\n",
    "            finance+=1\n",
    "        elif i == 'relationship':\n",
    "            relationship+=1\n",
    "        elif i == 'work':\n",
    "            work+=1\n",
    "    return family,finance,school,relationship,work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 1, 0, 0)"
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     "execution_count": 35,
     "metadata": {},
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   ],
   "source": [
    "causes(text1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_polarity(text):\n",
    "    #determines the polarity of each answer given by an individual\n",
    "    tokens = tokenizer.encode(text, return_tensors='pt')\n",
    "    result = model(tokens)\n",
    "    return int(torch.argmax(result.logits))+1\n",
    "\n",
    "def avg_sentiment(lis1,lis2):\n",
    "    f_val=[]\n",
    "    #determines the average putting into consideration weight\n",
    "    for i,j in zip(lis1, lis2):\n",
    "        val1=predict_polarity(i)*j\n",
    "        f_val.append(val1)\n",
    "\n",
    "    avg_f_val=sum(f_val)/len(f_val)\n",
    "    return avg_f_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.333333333333334"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que1='Lost everything today'\n",
    "que2='fuck off, Iwas insulted by my best friend'\n",
    "que3= 'pain and misery'\n",
    "\n",
    "que=[que1, que2, que3]\n",
    "weight=[20,15,5]\n",
    "\n",
    "avg_sentiment(que,weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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